CN112102098B - Data processing method, device, electronic equipment and storage medium - Google Patents
Data processing method, device, electronic equipment and storage medium Download PDFInfo
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- G06Q—INFORMATION AND COMMUNICATION TECHNOLOGY [ICT] SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL OR SUPERVISORY PURPOSES; SYSTEMS OR METHODS SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL OR SUPERVISORY PURPOSES, NOT OTHERWISE PROVIDED FOR
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Abstract
The application provides a data processing method, a device, electronic equipment and a storage medium, which are applied to the technical field of data processing, wherein the method comprises the following steps: acquiring medical expense data to be processed; taking the money with the first similarity greater than or equal to the first similarity threshold value in the money of the medical expense data and the second similarity smaller than the first similarity threshold value in the memory library as the unknown money; taking the money with the third similarity greater than or equal to a second similarity threshold value in the unknown money and the first abnormal money of the standard library as a second deducted money, wherein the second similarity threshold value is smaller than the first similarity threshold value; and carrying out fee deduction on the medical fee data according to the first deduction money and the second deduction money. The scheme not only saves the labor cost required by deducting the medical expense data, but also improves the efficiency of deducting the medical expense data.
Description
Technical Field
The present application relates to the field of data processing technologies, and in particular, to a data processing method, a data processing device, an electronic device, and a storage medium.
Background
With the popularization of information technology, an insurance company can be in information butt joint with a hospital, and when a user needs to reimburse medical fees through insurance, the insurance company can acquire various medical fee data of the user from the hospital on line to settle claims for the user, and the user is not required to arrange a plurality of medical fee files and notes by himself to find the insurance company to settle claims.
Although the medical expense data of the user can be flexibly interacted between the insurance company and the hospital, if the claim settlement is required to be completed, unreasonable charges in the medical expense data are required to be deducted, so that the claim settlement expense of the insurance company is reduced, the claim settlement personnel of the insurance company are required to audit the medical expense data one by one, a great deal of labor cost is required to be consumed, and the audit efficiency of unreasonable expenses is low, so that the deduction efficiency of the medical expense data is reduced.
Disclosure of Invention
In view of this, a first aspect of the present application provides a data processing method, the method comprising:
acquiring medical expense data to be processed;
Taking the money with the first similarity greater than or equal to a first similarity threshold value in the money of the medical expense data as a first deducted money, and taking the money with the second similarity smaller than the first similarity threshold value in the memory library in the money of the medical expense data as an unknown money;
taking the money of which the third similarity with the first-level abnormal money of the standard library is greater than or equal to a second similarity threshold value in the unknown money as a second deducted money, wherein the second similarity threshold value is smaller than the first similarity threshold value, and the first-level abnormal money is associated with at least one second-level abnormal money;
and carrying out fee deduction on the medical fee data according to the first deduction money and the second deduction money.
Optionally, the step of taking the money with the first similarity greater than or equal to the first similarity threshold value in the money of the medical fee data as the first deducted money, and taking the money with the second similarity smaller than the first similarity threshold value in the money of the medical fee data as the unknown money, wherein the second similarity between the second abnormal money and the compliance money in the memory bank is smaller than the first similarity threshold value, includes:
Acquiring a first similarity of the payment of the medical expense data and each second-level abnormal payment in the memory bank, and a second similarity of the payment of the medical expense data and each second-level abnormal payment and the compliance payment in the memory bank;
taking the money of the medical expense data as a first deduction money under the condition that the maximum value of the first similarity is larger than or equal to a first similarity threshold value;
taking the money of the medical expense data as an unknown money under the condition that the maximum value of the second similarity is smaller than a first similarity threshold value;
and taking the money with the third similarity greater than or equal to a second similarity threshold value in the unknown money and the first abnormal money of the standard library as a second deducted money, wherein the second similarity threshold value is smaller than the first similarity threshold value, and the method comprises the following steps:
obtaining a third similarity between the unknown money and each first-level abnormal money in the standard library;
and taking the unknown money as a second deduction money under the condition that the maximum value of the third similarity is larger than or equal to a second similarity threshold value.
Optionally, after the obtaining the third similarity between the unknown payment and each level of abnormal payment in the standard library, the method further includes:
Obtaining an audit result of the unknown money under the condition that the maximum value of the third similarity is smaller than a second similarity threshold value;
taking the unknown money as a third deduction money under the condition that the auditing result is of an abnormal type;
and carrying out fee deduction on the medical fee data according to the first deduction money and the third deduction money.
Optionally, before the acquiring the medical expense data to be processed, the method further includes:
acquiring first sample medical cost data;
the first sample medical expense data is combined in the same type to obtain second sample medical expense data;
obtaining a labeling result of the second sample medical expense data;
taking the second sample medical cost data with the marked result of the abnormal type as a second-level abnormal money, and taking the second sample medical cost data with the checked result of the compliance type as a compliance money to obtain a memory bank;
clustering the second-level abnormal money according to a third similarity threshold to obtain an abnormal money set;
extracting keywords of the abnormal money set;
and taking the keywords as first-level abnormal money related to second-level abnormal money in the abnormal money set to obtain a standard library.
Optionally, after the keyword corresponding to the abnormal money set is used as the first-level abnormal money to obtain the standard library, the method further includes:
receiving a modification input of a target money in the first-level abnormal money of the standard library;
removing the target money from the first-level abnormal money of the standard library;
and changing the second-level abnormal money related to the target money in the memory bank into the compliant money.
Optionally, after the deducting the medical fee data according to the first deducting money and the second deducting money, the method further includes:
marking deduction description information on the deducted medical expense data to obtain medical expense deduction details;
displaying the medical expense deduction details.
Optionally, before the acquiring the medical expense data to be processed, the method further includes:
receiving a medical fee deduction request, wherein the medical fee deduction request at least comprises: a medical case identification;
the obtaining medical expense data to be processed comprises the following steps:
acquiring medical detail fees and total discharge fees from a database of the hospital indicated by the medical case identifier;
and taking the medical detail expense as medical expense data to be processed when the difference between the sum of the medical detail expense and the discharge total expense is smaller than a difference threshold value.
According to a second aspect of the present application there is provided a data processing apparatus, the apparatus comprising:
the acquisition module is used for acquiring medical expense data to be processed;
the first processing module is used for taking the money with the first similarity larger than or equal to a first similarity threshold value in the money of the medical fee data as a first deducted money, and taking the money with the second similarity smaller than the first similarity threshold value in the money of the medical fee data as an unknown money;
the second processing module is used for taking the money, of the unknown money, of which the third similarity with the first-level abnormal money of the standard library is larger than or equal to a second similarity threshold value, as a second deducted money, wherein the second similarity threshold value is smaller than the first similarity threshold value, and the first-level abnormal money is associated with at least one second-level abnormal money;
and the deduction module is used for deducting the medical expense data according to the first deduction money and the second deduction money.
Optionally, the first processing module is further configured to:
acquiring a first similarity of the payment of the medical expense data and each second-level abnormal payment in the memory bank, and a second similarity of the payment of the medical expense data and each second-level abnormal payment and the compliance payment in the memory bank;
Taking the money of the medical expense data as a first deduction money under the condition that the maximum value of the first similarity is larger than or equal to a first similarity threshold value;
taking the money of the medical expense data as an unknown money under the condition that the maximum value of the second similarity is smaller than a first similarity threshold value;
the second processing module includes:
obtaining a third similarity between the unknown money and each first-level abnormal money in the standard library;
and taking the unknown money as a second deduction money under the condition that the maximum value of the third similarity is larger than or equal to a second similarity threshold value.
Optionally, the first processing module is further configured to:
obtaining an audit result of the unknown money under the condition that the maximum value of the third similarity is smaller than a second similarity threshold value;
taking the unknown money as a third deduction money under the condition that the auditing result is of an abnormal type;
and carrying out fee deduction on the medical fee data according to the first deduction money and the third deduction money.
Optionally, the apparatus further includes:
a pre-generation module for:
acquiring first sample medical cost data;
The first sample medical expense data is combined in the same type to obtain second sample medical expense data;
obtaining a labeling result of the second sample medical expense data;
taking the second sample medical cost data with the marked result of the abnormal type as a second-level abnormal money, and taking the second sample medical cost data with the checked result of the compliance type as a compliance money to obtain a memory bank;
clustering the second-level abnormal money according to a third similarity threshold to obtain an abnormal money set;
extracting keywords of the abnormal money set;
and taking the keywords as first-level abnormal money related to second-level abnormal money in the abnormal money set to obtain a standard library.
Optionally, the apparatus further includes:
a modification module for:
receiving a modification input of a target money in the first-level abnormal money of the standard library;
removing the target money from the first-level abnormal money of the standard library;
and changing the second-level abnormal money related to the target money in the memory bank into the compliant money.
Optionally, the apparatus further includes:
a display module for:
marking deduction description information on the deducted medical expense data to obtain medical expense deduction details;
Displaying the medical expense deduction details.
Optionally, the acquiring module is further configured to:
receiving a medical fee deduction request, wherein the medical fee deduction request at least comprises: a medical case identification;
acquiring medical detail fees and total discharge fees from a database of the hospital indicated by the medical case identifier;
and taking the medical detail expense as medical expense data to be processed when the difference between the sum of the medical detail expense and the discharge total expense is smaller than a difference threshold value.
According to a third aspect of the present application there is provided an electronic device comprising a memory, a processor and a computer program stored on the memory and executable on the processor, the processor implementing the data processing method of any of the above first aspects when executing the computer program.
According to a fourth aspect of the present application there is provided a computer readable storage medium having stored thereon a computer program which when executed by a processor implements the data processing method of any of the first aspects.
Aiming at the prior art, the application has the following advantages:
according to the data processing method, the device, the electronic equipment and the storage medium, the second-level abnormal money in the money memory bank in the medical cost data and the compliance money are subjected to high-similarity matching, the first deduction money is determined, the unmatched unknown money is subjected to low-similarity matching with the first-level abnormal money in the standard bank of all hospitals, the second deduction money is determined, and finally the medical cost data is deducted through the first deduction money and the second deduction money, so that the labor cost required by deduction of the medical cost data is saved, and the deduction efficiency of the medical cost data is improved.
The foregoing description is only an overview of the present application, and is intended to be implemented in accordance with the teachings of the present application in order that the same may be more clearly understood and to make the same and other objects, features and advantages of the present application more readily apparent.
Drawings
Various other advantages and benefits will become apparent to those of ordinary skill in the art upon reading the following detailed description of the preferred embodiments. The drawings are only for purposes of illustrating the preferred embodiments and are not to be construed as limiting the application. Also, like reference numerals are used to designate like parts throughout the figures. In the drawings:
FIG. 1 is a flow chart of steps of a data processing method according to an embodiment of the present application;
FIG. 2 is a flowchart illustrating steps of another data processing method according to an embodiment of the present application;
FIG. 3 is a schematic diagram showing the effects of a data processing method according to an embodiment of the present application;
FIG. 4 is a diagram showing a second effect of a data processing method according to an embodiment of the present application;
FIG. 5 is a flow chart of steps of a method for preprocessing medical fees according to an embodiment of the present application;
FIG. 6 is a third embodiment of a data processing method according to the present application;
FIG. 7 is a block diagram of a data processing apparatus according to an embodiment of the present application;
fig. 8 is a block diagram of an electronic device according to an embodiment of the present application.
Detailed Description
Exemplary embodiments of the present application will be described in more detail below with reference to the accompanying drawings. While exemplary embodiments of the present application are shown in the drawings, it should be understood that the present application may be embodied in various forms and should not be limited to the embodiments set forth herein. Rather, these embodiments are provided so that this disclosure will be thorough and complete, and will fully convey the scope of the application to those skilled in the art.
Fig. 1 is a flowchart of steps of a data processing method according to an embodiment of the present application, where the method includes:
step 101, acquiring medical expense data to be processed.
In the embodiment of the application, the medical expense data to be processed refers to expense data of medicines, hospitalization, manpower and the like generated by a user during treatment of a hospital, and generally, the medical expense data comprises a payment name and a payment amount.
After the user finishes the treatment in the hospital, the medical expense can be paid through the client of the insurance company, and the background server of the insurance company can acquire the medical expense data of the user during the treatment through the database of the hospital. However, in order to avoid that some unreasonable fees may be stored in the medical fee data, if the medical fee data to be processed is directly subjected to claim settlement and payment, additional expense is brought to the insurance company, and therefore, unreasonable fees in the medical fee data need to be identified and deducted. The background server of the insurance company is in communication connection with the databases of the i hospitals in advance, and can directly extract the medical expense data from the database of the j-th hospital in the i hospitals, wherein i and j are positive integers, and i is more than or equal to j.
Step 102, taking the money with the first similarity greater than or equal to the first similarity threshold value in the money of the medical fee data and the second similarity smaller than the first similarity threshold value in the money of the memory library as the unknown money.
In the embodiment of the application, the memory bank comprises the secondary abnormal charges and the compliance charges of all connected hospitals, the secondary abnormal charges refer to the original names of the abnormal charges of the hospitals, and it can be understood that the charges of the same charging item may be different for different hospitals, so that a plurality of corresponding secondary abnormal charges may exist for the same charging item due to the secondary abnormal charges of different hospitals stored in the memory bank. The compliant money refers to reasonable charging money in the connected hospitals, the compliant money in the memory bank and the second-level abnormal money in the memory bank are the same, and different compliant money of a plurality of hospitals can exist for the same charging item. Each hospital has a unique memory bank. The first similarity is the similarity between the medical expense data and the second-level abnormal expense, and the second similarity is the similarity between the medical expense data and the second-level abnormal expense and the compliance. The first similarity threshold is the maximum similarity between the money of the medical expense data and the money in the memory bank, and can be specifically determined according to actual requirements, which is not limited herein.
In practical applications, the memory bank is used for directly inquiring the money in the medical expense data, so the first similarity threshold may be 100%. And comparing the similarity between the name of the money in the medical expense data and the name of the money in the memory library to obtain a first deducted money which is included in the second-level abnormal money and an unknown money which is not included in the abnormal money and the compliance money. It can be understood that the first deduction money matched with the second-level abnormal money can be determined as an unreasonable charge money of the hospital, and because the hospital can periodically add new money names and change money names, some unknown money which is not included in the second-level abnormal money and the compliant money can occur, and whether the unreasonable charge money is unreasonable charged money or not still needs to be further determined and deducted.
Of course, if the similarity between the money in the medical expense data and the money in the memory bank is greater than or equal to the first similarity threshold, no unknown money exists, and then the medical expense data is deducted directly according to the obtained first deduction money.
And 103, taking the money with the third similarity greater than or equal to a second similarity threshold value in the unknown money as a second deducted money, wherein the second similarity threshold value is smaller than the first similarity threshold value, and the first-level abnormal money is associated with at least one second-level abnormal money.
In the embodiment of the application, the standard library comprises the first-level abnormal charges of the connected hospitals, namely, the second-level abnormal charges of the connected hospitals are summarized in advance to generate the corresponding first-level abnormal charges of the second-level abnormal charges belonging to the same charging item, so that each charging item has the unique corresponding first-level abnormal charges.
The third similarity is the similarity between the unknown and first-order abnormal money. The second similarity threshold is the maximum similarity of the unknown deposit and the first-level abnormal deposit in the standard library, and may be greater than the first similarity threshold, for example, in the case that the first similarity threshold is 100%, the second similarity threshold may be 70%, 80%, 90%, etc., and may be specifically determined according to the actual requirement, which is not limited herein.
Since the unknown deposit cannot be queried in the memory bank through the first similarity threshold value higher than the second similarity threshold value, the searching capability can be improved by adopting the second similarity threshold value smaller than the first similarity threshold value through the standard bank containing the first-level abnormal deposit of all hospitals, so as to determine whether the unknown deposit is unreasonably charged. Specifically, since the charges belonging to the same charging item in the memory bank may be repeated, if the charges are matched in the memory bank through the lower second similarity threshold, a situation that a plurality of second-level abnormal charges are matched or the second-level abnormal charges and the compliance charges are simultaneously matched may occur, so that the matching result is abnormal, and whether the charges are reasonable or not cannot be accurately judged. Therefore, the matching is performed by adopting a smaller second similarity threshold value, the similarity matching is required to be performed in a standard library provided with a unique first-level abnormal money for the same charging item, and the condition that the matching result is abnormal is avoided, so that the accuracy of the matched result can be ensured.
And taking the money with the third similarity greater than or equal to the second similarity threshold value as the second deduction money.
Step 104, deducting the cost of the medical cost data according to the first deduction money and the second deduction money.
In the embodiment of the application, the corresponding money names in the medical cost data are removed by removing the obtained money names of the first deduction money and the second deduction money, and the amount of the money is deducted by the amounts of the first deduction money and the second deduction money, so that the medical cost data after deduction and unreasonable charge can be obtained.
According to the data processing method provided by the application, the second-level abnormal money in the money memory bank in the medical expense data and the compliant money are subjected to high-similarity matching, the first deduction money is determined, the unmatched unknown money is subjected to low-similarity matching with the first-level abnormal money in the standard bank of all hospitals, the second deduction money is determined, and finally the medical expense data is deducted through the first deduction money and the second deduction money, so that the labor cost required by deduction of the medical expense data is saved, and the deduction efficiency of the medical expense data is improved.
FIG. 2 is a flowchart illustrating steps of another data processing method according to an embodiment of the present application, where the method includes:
step 201, receiving a medical fee deduction request, wherein the medical fee deduction request at least comprises: medical case identification.
In the embodiment of the application, the medical expense deduction request is a medical case identification carried request sent by a user through a client of an insurance company. The medical case identification may be a medical case number of the user.
When the user handles the settlement of hospitalization, the user can input the medical case number through the client side of the hospital set by the insurance company to initiate the settlement, inquire the medical cost data generated during the treatment period, select the function option of deducting the unreasonable cost audit, and send the medical cost deduction request to the server through the client side.
Step 202, acquiring medical detail fees and total discharge fees from a database of the indicated hospitals of the medical case identifications.
In the embodiment of the application, the medical detail cost refers to various charging fees generated by a user during treatment, and the discharge total cost refers to the cost of discharge required settlement calculated by a hospital side. The medical case identification can be used for inquiring medical detail fees generated by a user during treatment and the total discharge fees calculated by the hospital in a database of the hospital.
And step 203, taking the medical detail expense as medical expense data to be processed in the case that the difference between the sum of the medical detail expense and the total expense discharged from the hospital is smaller than a difference threshold value.
In embodiments of the present application, the margin threshold refers to an amount that allows between the sum of medical detail costs and the sum of medical detail costs incurred if the user were to be treated and the total hospital discharge costs for accounting. It will be appreciated that if the difference between the sum of the total discharge costs and the medical detail costs calculated by the hospital exceeds the difference threshold, it is indicative that the record of the acquired medical detail costs is incomplete and that re-acquisition is required. At this time, the hospital can be informed to audit the medical detail cost and upload accurate medical detail cost. If the difference does not exceed the difference threshold, the acquired medical detail expense is complete, and the medical detail expense can be used as medical expense data for subsequent processing.
The embodiment of the application ensures the integrity of the acquired medical detail fees by comparing the sum of the acquired medical detail fees with the total hospital discharge fees calculated by the hospital.
Step 204, obtaining a first similarity between the payment of the medical expense data and each second-level abnormal payment in the memory bank, and a second similarity between the payment of the medical expense data and each second-level abnormal payment and the compliance payment in the memory bank.
In the embodiment of the application, the similarity between the names of the medical charges and each of the charges in the memory bank can be determined by comparing the names of the charges in the medical charges with the names of the charges in the memory bank in a text similarity manner, wherein the similarity comprises a first similarity between the names of the charges in the medical charges and the names of each of the two-level abnormal charges, and a second similarity between the names of the charges in the medical charges and the names of each of the two-level abnormal charges and the names of each of the compliance charges. Specifically, the similarity comparison between the names of the funds is to extract the keywords after the names of the funds are segmented, so as to obtain the word meaning characteristics of each name of the funds according to the feature vectors of the keywords, and then compare the word meaning characteristics of the names of the funds compared with the similarity to obtain the similarity between the names of the funds.
Step 205, taking the money of the medical expense data as a first deduction money when the maximum value of the first similarity is greater than or equal to a first similarity threshold.
In the embodiment of the application, as the similarity between the charges is higher, the accuracy of mutual matching is higher, so that the maximum value of the first similarity between the nth charge and each second-level abnormal charge is selected as the basis of whether the charges of the medical charge data are matched with the charges in the memory bank, and particularly when the maximum value of the first similarity is greater than or equal to the first similarity threshold value, the nth charge is determined to be the unreasonably charged charge and is used as the first deduction charge.
And step 206, taking the payment of the medical expense data as an unknown payment when the maximum value of the second similarity is smaller than the first similarity threshold value.
In the embodiment of the application, since the second similarity is the similarity between the nth deposit and each of the second-level abnormal deposit and the compliant deposit, if the maximum value of the second similarity is smaller than the first similarity threshold value, it can be determined that the nth deposit cannot be matched with the second-level abnormal deposit and the compliant deposit in the memory bank, that is, whether the nth deposit is unreasonable charged or not cannot be determined temporarily, and the nth deposit is taken as the unknown deposit to wait for further discrimination in the subsequent step.
Step 207, obtaining a third similarity between the unknown money and each level of abnormal money in the standard library.
In the embodiment of the application, the m unknown money cannot be judged through the memory bank, so that a standard bank comprising first-level abnormal money is further introduced to further perform similarity matching. The third similarity between the mth unknown payment and each first-level abnormal payment is obtained, and the obtaining manner of the third similarity may be similar to the obtaining manner of the similarity in step 204, which is not described herein.
Step 208, taking the unknown deposit as the second deduction deposit when the maximum value of the third similarity is greater than or equal to the second similarity threshold.
In the embodiment of the present application, similarly to step 205, where the maximum value of the third similarity is greater than or equal to the second similarity threshold, it is determined that the mth unknown payment is an unreasonably charged payment, as the second deducted payment, only the second similarity threshold is smaller than the first similarity threshold in step 205, which also considers that there is a certain difference between the names of the payments in different hospitals, and the smaller similarity threshold can effectively improve the efficiency of similarity matching.
According to the embodiment of the application, the similarity of the higher similarity threshold is carried out on the money of the medical expense data and the money in the memory bank, and then the similarity of the lower similarity threshold is carried out on the unmatched unknown money and the money in the standard bank, so that the efficiency of inquiring the money in the medical expense data is improved.
Step 209, deducting the cost of the medical cost data according to the first deduction money and the second deduction money.
This step is described in detail with reference to step 104, and will not be described in detail here.
And step 210, obtaining the auditing result of the unknown money under the condition that the maximum value of the third similarity is smaller than the second similarity threshold value.
In the embodiment of the application, if the maximum value of the third similarity of the unknown deposit and each first-level abnormal deposit in the standard library is smaller than the second similarity threshold value, the newly-increased deposit used by the hospital which is not connected before the unknown deposit can be determined, and whether the unknown deposit is an unreasonably charged deposit can not be determined through the memory library and the standard library.
Further, the unknown money can be marked and highlighted for output, and the auditing personnel can be given for manual auditing or the money auditing model can be given for automatic auditing, so that the auditing result of the unknown money can be obtained. The money auditing model is obtained by training sample money marked with reasonable money labels and unreasonable money labels in advance.
Step 211, taking the unknown money as a third deduction money under the condition that the auditing result is of an abnormal type.
In the embodiment of the application, the abnormal type is a type of money which is not reasonably charged. If the auditing result is of an abnormal type, the mth unknown money is determined to be unreasonably charged money, and the m unknown money is used as a third deduction money for deducting.
Step 212, deducting the cost of the medical cost data according to the first deduction money and the third deduction money.
This step is similar to the medical fee data deduction in step 104, and will not be described here.
According to the embodiment of the application, the unknown money which cannot be matched with the memory bank and the standard bank is output for auditing, so that whether the unknown money needs to be deducted or not is judged, and the method is not limited to the mode of matching the money of the existing memory bank and the money of the standard bank, so that the method can be suitable for newly adding the medical expense money, and the applicability of the method for deducting the medical expense is improved.
And 213, marking deduction description information on the deducted medical expense data to obtain medical expense deduction details.
In the embodiment of the present application, the deduction instruction information refers to a reason instruction of the first deduction money, the second deduction money or the third deduction money for deducting the medical cost, and the deduction instruction information of each deduction money may be preset, for example: the deduction amount is the cost of the medicine outside the treatment period of the user, and the deduction instruction information can be 'extra medicine charge outside the treatment period'.
Step 214, displaying the medical fee deduction details.
For example, referring to fig. 3, the user may query the medical fee deduction details including the amount of each of the charges, the similarity, and the deduction fee by inputting the charge number or the charge name, and may also view the deduction description information to understand the deduction cause of the deduction fee.
Further, referring to fig. 4, after the medical fee deduction detail is obtained, fee information including the deduction fee is displayed to the user to confirm the fee to the user, and the user can perform medical fee settlement by clicking the next lower left after confirming the fee.
In the embodiment of the application, the deduction description information is added to the deducted medical expense data, so that the deduction description information is displayed for a user to check, and the user can intuitively know the details of deduction of the medical expense.
Optionally, referring to fig. 5, before the step 201, the method further includes:
step 221, acquiring first sample medical cost data.
In the embodiment of the application, the first sample medical expense data can be expense detail data of the j-th hospital in the past medical claim settlement case, or can be obtained by acquiring the expense detail data of the j-th hospital in real time, or can be medical expense data generated by a user, and the method is not limited herein according to actual requirements.
Step 222, merging the same items of the first sample medical expense data to obtain second sample medical expense data.
In the embodiment of the application, the data with the same name of the money in the first sample medical expense data are combined with the similar items to obtain the second sample medical expense data, so that the same charging item which appears for a plurality of times can be treated as one money, and the subsequent required data processing amount is reduced. For example: for the same user, multiple times of using the medicine A in the treatment period are recorded as multiple pieces of medical expense data according to different using time, but the medical expense data of the multiple pieces of medicine A belong to the same money, and at the moment, the medical expense data of the medicine A can be combined into the same item to obtain one piece of medical expense data related to the medicine A.
Step 223, obtaining the labeling result of the second sample medical cost data.
In the embodiment of the application, the labeling result is a label whether the second sample medical cost data is reasonable, specifically, the labeling result is a compliance type for the second sample medical cost data with reasonable cost, and the labeling result is an abnormality type for the second sample medical cost data with unreasonable cost. The labeling result can be manually labeled or can be automatically labeled through a conventional label generation model, and the labeling result can be specifically determined according to actual requirements without limitation.
And 224, taking the second sample medical cost data with the marked result of the abnormal type as a second-level abnormal payment, and taking the second sample medical cost data with the checked result of the compliance type as the compliance payment to obtain a memory bank.
In the embodiment of the application, the second sample medical cost data of the compliance type of the jth hospital is summarized to be used as the compliance money; the medical expense data of the second sample of the abnormal type is summarized to be used as a second-level abnormal money, and then the combined money and the second-level abnormal money are stored together to obtain a memory bank of the j-th hospital.
And 225, clustering the second-level abnormal money according to a third similarity threshold to obtain an abnormal money set.
In the embodiment of the application, the two-level abnormal money in the memory bank is subjected to text similarity comparison to obtain the similarity between each two-level abnormal money, and the two-level abnormal money with the similarity larger than the third similarity threshold value is regarded as belonging to the same charging item and classified into the same abnormal money set.
And 226, extracting keywords of the abnormal money set.
In the embodiment of the present application, keywords that can describe the same charging item exist in the second-level abnormal money belonging to the same charging item, for example: the two second-level abnormal charges of the heating fee and the heating fee belong to the charge for the heating, so that the keyword 'warm' in the two abnormal charges can be used as the keyword. Of course, the specific keywords may be determined according to actual requirements, or may be selected manually, which is not limited herein.
Step 227, using the keyword as a first-level abnormal money associated with a second-level abnormal money in the abnormal money set, to obtain a standard library.
In the embodiment of the application, the keywords are used as first-level abnormal money, and the association relation between the keywords and second-level abnormal money is established, so that a standard library can be obtained, namely the standard library is a judging standard for judging whether the cost money of the connected hospitals is reasonable or not.
According to the application, the medical expense deduction efficiency is improved by carrying out structural storage in advance according to the first sample medical expense details of the hospitals as the standard library and the memory library corresponding to each hospital.
Step 228, receiving a modification input of the target money in the first-level abnormal money of the standard library.
In the embodiment of the application, the modification input is input of the types of clicking, long pressing, sliding, function floating window, voice, gesture and the like of the first-level abnormal money, and the modification input can be specifically determined according to actual requirements without limitation.
In practical applications, it is possible to change whether a certain charge item is reasonable, and therefore, when the charge item is reasonably changed, it is necessary to change the relevant charges in the memory bank and the standard bank efficiently.
And 229, removing the target money from the first-level abnormal money in the standard library.
In the embodiment of the application, when the same charging item is reasonably changed, if a plurality of corresponding second-level abnormal charges are searched in the memory bank one by one, the staff can search one by one, and the missing situation can occur, so that the modification is needed from the standard bank containing only the single first-level abnormal charges for the same charging item.
And 230, changing the second-level abnormal money in the memory bank, which is associated with the target money, into a compliant money.
In the embodiment of the application, when the target money for the first-level abnormal money is removed from the standard library, the second-level abnormal money related to the target money in the memory library is correspondingly changed into the compliant money, so that the money attributes in the standard library and the memory library are effectively changed.
According to the embodiment of the application, the primary abnormal money is removed from the standard library, and the secondary abnormal money in the corresponding memory library is changed into the compliant money, so that the memory library can be synchronously updated along with the updating of the standard library, and the database updating efficiency is improved.
For example, the user may view the determination result of each payment in the medical fee data through the interface shown in fig. 6, where the determination result includes information about the name of the payment, whether the payment is included in an abnormal payment, the matching object, the operator, the operation time, and the like. Specifically, for the first deducted money included in the first abnormal money, the user may also change the first deducted money 1 to the compliant money by clicking the "change to compliant money" option in the right operation option, and may also perform the similarity matching by selecting "auto matching". For unknown funds not contained in the memory bank, the user can perform similarity matching with the funds in the standard bank by clicking on the "auto-match" option on the right side.
According to the embodiment of the application, the synchronous mapping relation between the money in the memory bank and the money in the standard bank is established, and when the target money in the memory bank is changed, the target money in the standard bank is correspondingly updated synchronously, so that the effectiveness of the memory bank and the standard bank is ensured.
According to the other data processing method provided by the application, the second-level abnormal money in the money memory bank in the medical expense data and the compliant money are subjected to high-similarity matching, the first deduction money is determined, the unmatched unknown money is subjected to low-similarity matching with the first-level abnormal money in the standard bank of all hospitals, the second deduction money is determined, and finally the medical expense data is deducted through the first deduction money and the second deduction money, so that the labor cost required by deduction of the medical expense data is saved, and the deduction efficiency of the medical expense data is improved. When the medical expense data is acquired, the sum of the medical expense details is compared with the hospital-calculated discharge total expense, the integrity of the acquired medical expense details is ensured, and after the medical expense data is deducted, corresponding deduction instruction information is added to obtain the medical expense deduction details for a user to check, so that the user can conveniently know the details of medical expense deduction, and when the target money in the memory bank is changed, the target money in the standard bank is synchronously updated, and the effectiveness of the memory bank and the standard bank is ensured.
Fig. 7 is a data processing apparatus 30 according to an embodiment of the present application, the apparatus includes:
an acquisition module 301, configured to acquire medical expense data to be processed;
the first processing module 302 is configured to use, as a first deducted money, a money in which a first similarity between the money in the medical expense data and a second abnormal money in the memory bank is greater than or equal to a first similarity threshold, and use, as an unknown money, a money in which a second similarity between the money in the medical expense data and the second abnormal money in the memory bank and a compliance money is less than the first similarity threshold;
a second processing module 303, configured to use, as a second deducted money, a money in the unknown money, where a third similarity between the unknown money and a first-stage abnormal money in the standard library is greater than or equal to a second similarity threshold, the second similarity threshold is smaller than the first similarity threshold, and the first-stage abnormal money is associated with at least one second-stage abnormal money;
the deduction module 304 is configured to deduct the medical fee data according to the first deduction money and the second deduction money.
Optionally, the first processing module 302 is further configured to:
Acquiring a first similarity of the payment of the medical expense data and each second-level abnormal payment in the memory bank, and a second similarity of the payment of the medical expense data and each second-level abnormal payment and the compliance payment in the memory bank;
taking the money of the medical expense data as a first deduction money under the condition that the maximum value of the first similarity is larger than or equal to a first similarity threshold value;
taking the money of the medical expense data as an unknown money under the condition that the maximum value of the second similarity is smaller than a first similarity threshold value;
the second processing module 303 is further configured to:
obtaining a third similarity between the unknown money and each first-level abnormal money in the standard library;
and taking the unknown money as a second deduction money under the condition that the maximum value of the third similarity is larger than or equal to a second similarity threshold value.
Optionally, the first processing module 302 is further configured to:
obtaining an audit result of the unknown money under the condition that the maximum value of the third similarity is smaller than a second similarity threshold value;
taking the unknown money as a third deduction money under the condition that the auditing result is of an abnormal type;
And carrying out fee deduction on the medical fee data according to the first deduction money and the third deduction money.
Optionally, the apparatus further includes:
a pre-generation module for:
acquiring first sample medical cost data;
the first sample medical expense data is combined in the same type to obtain second sample medical expense data;
obtaining a labeling result of the second sample medical expense data;
taking the second sample medical cost data with the marked result of the abnormal type as a second-level abnormal money, and taking the second sample medical cost data with the checked result of the compliance type as a compliance money to obtain a memory bank;
clustering the second-level abnormal money according to a third similarity threshold to obtain an abnormal money set;
extracting keywords of the abnormal money set;
and taking the keywords as first-level abnormal money related to second-level abnormal money in the abnormal money set to obtain a standard library.
Optionally, the apparatus further includes:
a modification module for:
receiving a modification input of a target money in the first-level abnormal money of the standard library;
removing the target money from the first-level abnormal money of the standard library;
And changing the second-level abnormal money related to the target money in the memory bank into the compliant money.
Optionally, the apparatus further includes:
a display module for:
marking deduction description information on the deducted medical expense data to obtain medical expense deduction details;
displaying the medical expense deduction details.
Optionally, the acquiring module is further configured to:
receiving a medical fee deduction request, wherein the medical fee deduction request at least comprises: a medical case identification;
acquiring medical detail fees and total discharge fees from a database of the hospital indicated by the medical case identifier;
and taking the medical detail expense as medical expense data to be processed when the difference between the sum of the medical detail expense and the discharge total expense is smaller than a difference threshold value.
According to the data processing device, the second-level abnormal money in the money memory bank and the compliant money in the medical cost data are subjected to high-similarity matching, the first deduction money is determined, the unmatched unknown money is subjected to low-similarity matching with the first-level abnormal money in the standard bank of all hospitals, the second deduction money is determined, and finally the medical cost data are deducted through the first deduction money and the second deduction money, so that labor cost required by deduction of the medical cost data is saved, and the deduction efficiency of the medical cost data is improved.
For the embodiment of the server described above, since it is substantially similar to the method embodiment, the description is relatively simple, and reference is made to the description of the method embodiment in part.
The embodiment of the present application further provides an electronic device, as shown in fig. 8, including a processor 401, a communication interface 402, a memory 403, and a communication bus 404, where the processor 401, the communication interface 402, and the memory 403 perform communication with each other through the communication bus 404,
a memory 403 for storing a computer program;
the processor 401, when executing the program stored in the memory 403, implements the following steps:
acquiring medical expense data to be processed; taking the money with the first similarity greater than or equal to a first similarity threshold value in the money of the medical expense data as a first deducted money, and taking the money with the second similarity smaller than the first similarity threshold value in the memory library in the money of the medical expense data as an unknown money; taking the money of which the third similarity with the first-level abnormal money of the standard library is greater than or equal to a second similarity threshold value in the unknown money as a second deducted money, wherein the second similarity threshold value is smaller than the first similarity threshold value, and the first-level abnormal money is associated with at least one second-level abnormal money; and carrying out fee deduction on the medical fee data according to the first deduction money and the second deduction money.
The communication bus mentioned by the above terminal may be a peripheral component interconnect standard (Peripheral Component Interconnect, abbreviated as PCI) bus or an extended industry standard architecture (Extended Industry Standard Architecture, abbreviated as EISA) bus, etc. The communication bus may be classified as an address bus, a data bus, a control bus, or the like. For ease of illustration, the figures are shown with only one bold line, but not with only one bus or one type of bus.
The communication interface is used for communication between the terminal and other devices.
The memory may include random access memory (Random Access Memory, RAM) or non-volatile memory (non-volatile memory), such as at least one disk memory. Optionally, the memory may also be at least one memory device located remotely from the aforementioned processor.
The processor may be a general-purpose processor, including a central processing unit (Central Processing Unit, CPU for short), a network processor (Network Processor, NP for short), etc.; but also digital signal processors (Digital Signal Processing, DSP for short), application specific integrated circuits (Application Specific Integrated Circuit, ASIC for short), field-programmable gate arrays (Field-Programmable Gate Array, FPGA for short) or other programmable logic devices, discrete gate or transistor logic devices, discrete hardware components.
In yet another embodiment of the present application, a computer readable storage medium is provided, in which instructions are stored, which when run on a computer, cause the computer to perform the data processing method according to any of the above embodiments.
In a further embodiment of the present application, a computer program product comprising instructions which, when run on a computer, causes the computer to perform the data processing method according to any of the above embodiments is also provided.
In the above embodiments, it may be implemented in whole or in part by software, hardware, firmware, or any combination thereof. When implemented in software, may be implemented in whole or in part in the form of a computer program product. The computer program product includes one or more computer instructions. When loaded and executed on a computer, produces a flow or function in accordance with embodiments of the present application, in whole or in part. The computer may be a general purpose computer, a special purpose computer, a computer network, or other programmable apparatus. The computer instructions may be stored in or transmitted from one computer-readable storage medium to another, for example, by wired (e.g., coaxial cable, optical fiber, digital Subscriber Line (DSL)), or wireless (e.g., infrared, wireless, microwave, etc.). The computer readable storage medium may be any available medium that can be accessed by a computer or a data storage device such as a server, data center, etc. that contains an integration of one or more available media. The usable medium may be a magnetic medium (e.g., floppy Disk, hard Disk, magnetic tape), an optical medium (e.g., DVD), or a semiconductor medium (e.g., solid State Disk (SSD)), etc.
It is noted that in the text, relational terms such as first and second, and the like are used solely to distinguish one entity or action from another entity or action without necessarily requiring or implying any actual such relationship or order between such entities or actions. Moreover, the terms "comprises," "comprising," or any other variation thereof, are intended to cover a non-exclusive inclusion, such that a process, method, article, or apparatus that comprises a list of elements does not include only those elements but may include other elements not expressly listed or inherent to such process, method, article, or apparatus. Without further limitation, an element defined by the phrase "comprising one … …" does not exclude the presence of other like elements in a process, method, article, or apparatus that comprises the element.
In this specification, each embodiment is described in a related manner, and identical and similar parts of each embodiment are all referred to each other, and each embodiment mainly describes differences from other embodiments. In particular, for system embodiments, since they are substantially similar to method embodiments, the description is relatively simple, as relevant to see a section of the description of method embodiments.
The foregoing description is only of the preferred embodiments of the present application and is not intended to limit the scope of the present application. Any modification, equivalent replacement, improvement, etc. made within the spirit and principle of the present application are included in the protection scope of the present application.
Claims (10)
1. A method of data processing, the method comprising:
acquiring medical expense data to be processed;
taking the money with the first similarity greater than or equal to a first similarity threshold value in the money of the medical expense data as a first deducted money, and taking the money with the second similarity smaller than the first similarity threshold value in the memory library in the money of the medical expense data as an unknown money; the memory library comprises two-level abnormal charges and a combined charge of all connected hospitals, wherein the two-level abnormal charges refer to original names of abnormal charges of the hospitals which are not reasonably charged, and the combined charge refers to reasonable charged charges in the connected hospitals; comparing the names of the funds in the medical fee data with the names of the funds in the memory bank in a text similarity mode, so that the similarity of the funds in the medical fee and each funds in the memory bank is determined, wherein the similarity comprises the first similarity and the second similarity;
Taking the money of which the third degree of similarity with the first-level abnormal money of the standard library is larger than or equal to a second similarity threshold value in the unknown money as a second deducted money, wherein the second similarity threshold value is smaller than the first similarity threshold value, and the first-level abnormal money is associated with at least one second-level abnormal money; the standard library comprises first-level abnormal charges of the connected hospitals, wherein the first-level abnormal charges are corresponding first-level abnormal charges generated by summarizing second-level abnormal charges of the connected hospitals in advance, and the second-level abnormal charges belong to the same charge item;
and carrying out fee deduction on the medical fee data according to the first deduction money and the second deduction money.
2. The method according to claim 1, wherein the step of taking, as a first deducted money, a money of the medical fee data having a first similarity with a second-level abnormal money of a memory bank greater than or equal to a first similarity threshold, and taking, as an unknown money, a money of the medical fee data having a second similarity with the second-level abnormal money of the memory bank and a compliance money less than the first similarity threshold, includes:
Acquiring a first similarity of the payment of the medical expense data and each second-level abnormal payment in the memory bank, and a second similarity of the payment of the medical expense data and each second-level abnormal payment and the compliance payment in the memory bank;
taking the money of the medical expense data as a first deduction money under the condition that the maximum value of the first similarity is larger than or equal to a first similarity threshold value;
taking the money of the medical expense data as an unknown money under the condition that the maximum value of the second similarity is smaller than a first similarity threshold value;
and taking the money with the third similarity greater than or equal to a second similarity threshold value in the unknown money and the first abnormal money of the standard library as a second deducted money, wherein the second similarity threshold value is smaller than the first similarity threshold value, and the method comprises the following steps:
obtaining a third similarity between the unknown money and each first-level abnormal money in the standard library;
and taking the unknown money as a second deduction money under the condition that the maximum value of the third similarity is larger than or equal to a second similarity threshold value.
3. The method of claim 2, further comprising, after said obtaining a third similarity of said unknown deposit to each of said primary abnormal deposits in said standard library:
Obtaining an audit result of the unknown money under the condition that the maximum value of the third similarity is smaller than a second similarity threshold value;
taking the unknown money as a third deduction money under the condition that the auditing result is of an abnormal type;
and carrying out fee deduction on the medical fee data according to the first deduction money and the third deduction money.
4. The method of claim 1, further comprising, prior to the acquiring medical cost data to be processed:
acquiring first sample medical cost data;
the first sample medical expense data is combined in the same type to obtain second sample medical expense data;
obtaining a labeling result of the second sample medical expense data;
taking the second sample medical cost data with the marked result of the abnormal type as a second-level abnormal money, and taking the second sample medical cost data with the checked result of the compliance type as a compliance money to obtain a memory bank;
clustering the second-level abnormal money according to a third similarity threshold to obtain an abnormal money set;
extracting keywords of the abnormal money set;
and taking the keywords as first-level abnormal money related to second-level abnormal money in the abnormal money set to obtain a standard library.
5. The method of claim 4, further comprising, after the obtaining the standard library as the first level of the abnormal money associated with the second level of the abnormal money set:
receiving a modification input of a target money in the first-level abnormal money of the standard library;
removing the target money from the first-level abnormal money of the standard library;
and changing the second-level abnormal money related to the target money in the memory bank into the compliant money.
6. The method of claim 1, further comprising, after said deducting the medical cost data according to the first deduction and the second deduction:
marking deduction description information on the deducted medical expense data to obtain medical expense deduction details;
displaying the medical expense deduction details.
7. The method of claim 1, further comprising, prior to the acquiring medical cost data to be processed:
receiving a medical fee deduction request, wherein the medical fee deduction request at least comprises: a medical case identification;
the obtaining medical expense data to be processed comprises the following steps:
Acquiring medical detail fees and total discharge fees from a database of the hospital indicated by the medical case identifier;
and taking the medical detail expense as medical expense data to be processed when the difference between the sum of the medical detail expense and the discharge total expense is smaller than a difference threshold value.
8. A product testing device, comprising:
the acquisition module is used for acquiring medical expense data to be processed;
the first processing module is used for taking the money with the first similarity larger than or equal to a first similarity threshold value in the money of the medical fee data as a first deducted money, and taking the money with the second similarity smaller than the first similarity threshold value in the money of the medical fee data as an unknown money; the memory library comprises two-level abnormal charges and a combined charge of all connected hospitals, wherein the two-level abnormal charges refer to original names of abnormal charges of the hospitals which are not reasonably charged, and the combined charge refers to reasonable charged charges in the connected hospitals; comparing the names of the funds in the medical fee data with the names of the funds in the memory bank in a text similarity mode, so that the similarity of the funds in the medical fee and each funds in the memory bank is determined, wherein the similarity comprises the first similarity and the second similarity;
The second processing module is used for taking the money, of the unknown money, with the third similarity of the first-level abnormal money of the standard library being greater than or equal to a second similarity threshold, as a second deducted money, wherein the second similarity threshold is smaller than the first similarity threshold, and the first-level abnormal money is associated with at least one second-level abnormal money; the standard library comprises first-level abnormal charges of the connected hospitals, wherein the first-level abnormal charges are corresponding first-level abnormal charges generated by summarizing second-level abnormal charges of the connected hospitals in advance, and the second-level abnormal charges belong to the same charge item;
and the deduction module is used for deducting the medical expense data according to the first deduction money and the second deduction money.
9. An electronic device comprising a memory, a processor and a computer program stored on the memory and executable on the processor, the processor implementing the data processing method of any of claims 1 to 7 when the computer program is executed.
10. A computer-readable storage medium, on which a computer program is stored, which computer program, when being executed by a processor, implements the data processing method of any of claims 1 to 7.
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